Summary and Info
This is the first book to simplify atmospheric predictions enabling laypersons to make their own derivative forecasts. Scientists and engineers can learn to predict weather-dependent phenomena to assess the risks associated with decisions in the construction and operation phases of water resource planning. This self-educating method simultaneously uses probabilistic meteorology forecasts over different time scales, time periods, spatial domains, probability statements, and meteorology variables. This is a practical, hands-on guide with comprehensive and straightforward theory, procedures, and examples for using short-term, seasonal, and interannual forecasts of meteorology probabilities, available from the National Oceanic and Atmospheric Administration, Environment Canada, and other agencies. The examples use different hydrology models; employ both user-defined and agency-produced meteorology probability forecasts in the United States and Canada; illustrate El Nino and La Nina conditional probabilities and examples of their derivation; and provide sufficient information for the reader's own applications. An extensive appendix describes the acquisition, installation, and use of freely available software to prepare historical files for individualized applications, to input forecast meteorology probabilities of a specific site, to extract reference quantile estimates, to prioritize forecasts, and to solve the resulting set of equations for derivative forecasts
More About the Author
The Cooley–Tukey algorithm, named after J.W. Cooley and John Tukey, is the most common fast Fourier transform (FFT) algorithm.
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